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In this paper we develop a new perspective on generalization of neural networks by proposing and investigating the concept of a neural network stiffness.
Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
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MNIST handwritten digit database
Yann LeCun and Corinna Cortes · 2010
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On the number of linear regions of deep neural networks
Guido Montúfar, Razvan Pascanu, Kyunghyun Cho, and Yoshua Bengio · 2014
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Deep residual learning for image recognition, 2015
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
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Exponential expressivity in deep neural networks through transient chaos
Ben Poole, Subhaneil Lahiri, Maithreyi Raghu, Jascha Sohl-Dickstein, and Surya Ganguli · 2016
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Understanding deep learning requires rethinking generalization
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals · 2016
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Samuel S. Schoenholz, Justin Gilmer, Surya Ganguli, and Jascha Sohl-Dickstein · 2016
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On the expressive power of deep neural networks
Maithra Raghu, Ben Poole, Jon Kleinberg, Surya Ganguli, and Jascha Sohl-Dickstein · 2017
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A closer look at memorization in deep networks
Devansh Arpit, Stanislaw K. Jastrzebski, Nicolas Ballas, David Krueger, Emmanuel Bengio, Maxinder S. Kanwal, Tegan Maharaj, Asja Fischer, Aaron C. Courville, Yoshua Bengio, and Simon Lacoste-Julien · 2017
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Nonlinear random matrix theory for deep learning
Jeffrey Pennington and Pratik Worah · 2017
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Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
Han Xiao, Kashif Rasul, and Roland Vollgraf · 2017
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A broad-coverage challenge corpus for sentence understanding through inference
Adina Williams, Nikita Nangia, and Samuel R. Bowman · 2017
Cited alongside, same era.
On the optimization of deep networks: Implicit acceleration by overparameterization
Sanjeev Arora, Nadav Cohen, and Elad Hazan · 2018
Neural tangent kernel: Convergence and generalization in neural networks, 2018
Arthur Jacot, Franck Gabriel, and Clément Hongler · 2018
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BERT: pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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The goldilocks zone: Towards better understanding of neural network loss landscapes
Stanislav Fort and Adam Scherlis · 2019
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Large scale structure of neural network loss landscapes, 2019
Stanislav Fort and Stanislaw Jastrzebski · 2019
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An investigation into neural net optimization via hessian eigenvalue density, 2019
Behrooz Ghorbani, Shankar Krishnan, and Ying Xiao · 2019
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Cited alongside, same era.
Measuring the intrinsic dimension of objective landscapes
Chunyuan Li, Heerad Farkhoor, Rosanne Liu, and Jason Yosinski · 2018
Cited alongside, same era.
On the Spectral Bias of Neural Networks
Nasim Rahaman, Aristide Baratin, Devansh Arpit, Felix Draxler, Min Lin, Fred A. Hamprecht, Yoshua Bengio, and Aaron Courville · 2018
Cited alongside, same era.
Sensitivity and generalization in neural networks: an empirical study
Roman Novak, Yasaman Bahri, Daniel A. Abolafia, Jeffrey Pennington, and Jascha Sohl-Dickstein · 2018
Cited alongside, same era.
Gradient Descent Finds Global Minima of Deep Neural Networks
Simon S. Du, Jason D. Lee, Haochuan Li, Liwei Wang, and Xiyu Zhai
Cited in the paper.
Gradient Descent Provably Optimizes Over-parameterized Neural Networks
Simon S. Du, Xiyu Zhai, Barnabas Poczos, and Aarti Singh
Cited in the paper.
Stanisław Jastrzębski, Zachary Kenton, Nicolas Ballas, Asja Fischer, Yoshua Bengio, and Amost Storkey · 2019
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Measurements of three-level hierarchical structure in the outliers in the spectrum of deepnet hessians
Vardan Papyan · 2019
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Emergent properties of the local geometry of neural loss landscapes, 2019
Stanislav Fort and Surya Ganguli · 2019
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